Another major disadvantage of existing methods is that these methods use cross-entropy loss without additional constraints for training. Recently, [1]} stated that traditional cross-entropy based methods assume that all instances within each category should be close in feature distribution. This ignores the unique information of each sample. Thus, cross-entropy loss encourages the model to extract similar features for same category. This might be helpful for classification or segmentation of datasets such as Imagenet or Cityscapes, where objects of the same category should have similar features. However, in the case of image forgery localization, extracting similar features for all the tampered regions in the dataset is not optimal as different manipulation operations leave behind different forgery footprints in the tampered regions. Hence, without additional constraints, a common cross-entropy loss-based framework is prone to over-fitting on specific forgery patterns [2]}. This is not conducive to generalization.
